Stream Computing Moves Forward

IBM will sell software that analyzes data in real time.

May 21, 2009

To some engineers at IBM, the traditional approach to
software analysis is far too inefficient. Data is collected and stored in a
repository, and then software breaks off chunks of it to analyze. Some time
later, the software spits out a result. But recent work at IBM is
providing a better way: analyze the data as it’s collected. The concept is
called stream computing and it could revolutionize industries like finance,
health care, and weather monitoring, where real-time data and analysis can help
people make better, faster decisions.

In April, IBM showed off a system
that could analyze the constantly fluctuating value of stocks. Now the company is working toward developing a product, called System
S, that could be applied to any field in which numbers need to be crunched
quickly.

I.B.M., based in Armonk, N.Y., spent close to six
years working on the software and has just moved to start selling a product
based on it called System S. The company expects it to encourage breakthroughs
in fields like finance and city management by helping people better understand
patterns in data.

…

Instead
of creating separate large databases to track things like currency movements,
stock trading patterns and housing data, the System S software can meld all of
that information together. In addition, it could theoretically then layer on
databases that tracked current events, like news headlines on the Internet or
weather fluctuations, to try to gauge how such factors interplay with the
financial data.

Tagged

I’m a freelance science and technology journalist based in San Francisco. I was the information technology editor at MIT Technology Review from 2005 to 2009, where I wrote more than 350 stories about emerging technologies in areas that include… More computers, mobile devices, displays, communication networks, Internet startups, and more.
I was an integral part of a technology trend-spotting team, highlighting early work in reality mining, plasmonics, adaptable networks, and racetrack memory. I’ve contributed to The Economist, U.S News & World Report, Gizmodo, New Scientist, Science News, and SELF, among other publications. And I’m currently working on a book with Nathan Eagle called Reality Mining: Using Big Data to Engineer a Better World (MIT Press).

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